Exactly the goal of our open ended projects! @BrownCSDept
Seeing students enjoy it worths every effort you pour into course prep.
Here is why I love teaching CS/Optimization:
Forge: Foundational Optimization Representations from Graph Embeddings.
#Forge is a self-supervised method for embedding optimization problems, just like your favorite text embedding.
A single pre-trained model works across tasks, problems, and sizes 🤯 https://t.co/HWBoyEnnan
📢 If you're solving real-world problems with AI, we want to hear from you!
As Vice Chair of IAAI'26, I invite you to submit your work to Innovative Applications of AI 2026 🚀
🗓️ Deadline: Aug 18, 2025
📍 Conference: Jan 2026, Singapore as part of #AAAI https://t.co/V8aVdIgYJs
@lauriewired You just made me remember the summer I spent at AVM, the ActionScript Virtual Machine team at Adobe (really strong engineering team btw) where I studied the AVM specs line by line, and wrote a fuzzer based on grammar guided genetic algorithm —discovered some deep vulnerabilities
📢📷Tomorrow (January 14) at 10:00, we will host from Serdar Kadioglu from @BrownUniversity: “Toward Modeling Assistants: Bridging Natural Language and Optimization”
Zoom info: [email protected] or just DM!
Driven by the complexity and the sheer scale of real-world problems in industry while seeking their abstractions & generalizations.
Check out our latest applied research at @Fidelity Ai Center
#ai#ml#fidelityassociate
https://t.co/uxU2d0LkEL
@mervenoyann@jmhessel Others already mentioned Optverse and Optiguide —which are very cool.
And there is an upcoming workshop at AAAI’24 exactly on this topic
https://t.co/5fIrKR0xML
@mervenoyann@jmhessel Ner4Opt is avail via ‘pip install ner4opt’ but it only extracts entities without building the complete model.
The position paper on Holy Grail 2.0 (CP’23) shows a decomposition guided prompting to generate constraint models —results are very promising https://t.co/9EmIp0alRS